Upload 3 files

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+# Load the model
+model = load_model('cnn_model.h5', compile=False)
+
+# Function to process the uploaded image
+def process_image(img):
+    img = img.resize((64, 64))
+    img = np.array(img)
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+# Custom CSS for better design
+st.markdown("""
+    <style>
+        .main { 
+            background-color: #f0f4f7; 
+            padding: 2rem;
+            border-radius: 10px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        }
+        h1 {
+            color: #4caf50;
+            font-size: 36px;
+            font-weight: bold;
+            text-align: center;
+        }
+        .description {
+            font-size: 18px;
+            color: #555;
+            text-align: center;
+            margin-bottom: 2rem;
+        }
+        .stButton>button {
+            background-color: #4caf50;
+            color: white;
+            font-size: 18px;
+            border-radius: 5px;
+            padding: 10px 20px;
+            margin-top: 20px;
+            border: none;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        }
+        .stButton>button:hover {
+            background-color: #45a049;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Title of the application
+st.title('Glass Detection from Image 📸')
+
+# Brief description
+st.markdown('<p class="description">Upload a photo, and the model will predict whether there is a glass or not.</p>', unsafe_allow_html=True)
+
+# File uploader for the user to upload an image
+file = st.file_uploader('Select an image (jpg, jpeg, png)', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    # Displaying the uploaded image
+    img = Image.open(file)
+    st.image(img, caption='Uploaded Image', use_column_width=True)
+
+    # Processing the image
+    image = process_image(img)
+    prediction = model.predict(image)
+
+    # Get the predicted class index
+    predicted_class = np.argmax(prediction, axis=-1)
+
+    # Displaying prediction result
+    st.subheader("Prediction Result:")
+
+    if predicted_class == 1:
+        prediction_text = '✅ There is a glass'
+    else:
+        prediction_text = '❌ There is no a glass'
+
+    st.write(prediction_text)
+
+else:
+    st.write("Please upload an image to get started.")
+
+# Footer
+st.markdown("<p style='text-align: center; font-size: 12px; color: #888;'>Built with 💚 by Senasu Demir</p>", unsafe_allow_html=True)

cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ceb8653b7e4936594513b43be66d4c155d8d1502e5fd2ffe7c22185d5a02ba48
+size 134058560

requirements.txt

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+streamlit
+tensorflow
+Pillow